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Thermodynamic computing system for AI applications

Author

Listed:
  • Denis Melanson

    (Normal Computing Corporation)

  • Mohammad Abu Khater

    (Normal Computing Corporation)

  • Maxwell Aifer

    (Normal Computing Corporation)

  • Kaelan Donatella

    (Normal Computing Corporation)

  • Max Hunter Gordon

    (Normal Computing Corporation)

  • Thomas Ahle

    (Normal Computing Corporation)

  • Gavin Crooks

    (Normal Computing Corporation)

  • Antonio J. Martinez

    (Normal Computing Corporation)

  • Faris Sbahi

    (Normal Computing Corporation)

  • Patrick J. Coles

    (Normal Computing Corporation)

Abstract

Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for alternative computing hardware in order to truly unlock the potential for AI. Physics-based hardware, such as thermodynamic computing, has the potential to provide a fast, low-power means to accelerate AI primitives, especially generative AI and probabilistic AI. In this work, we present a small-scale thermodynamic computer, which we call the stochastic processing unit. This device is composed of RLC circuits, as unit cells, on a printed circuit board, with 8 unit cells that are all-to-all coupled via switched capacitances. It can be used for either sampling or linear algebra primitives, and we demonstrate Gaussian sampling and matrix inversion on our hardware. The latter represents a thermodynamic linear algebra experiment. We envision that this hardware, when scaled up in size, will have significant impact on accelerating various probabilistic AI applications.

Suggested Citation

  • Denis Melanson & Mohammad Abu Khater & Maxwell Aifer & Kaelan Donatella & Max Hunter Gordon & Thomas Ahle & Gavin Crooks & Antonio J. Martinez & Faris Sbahi & Patrick J. Coles, 2025. "Thermodynamic computing system for AI applications," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59011-x
    DOI: 10.1038/s41467-025-59011-x
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